Competitive Programming

Competitive Programming, available at $44.99, has an average rating of 4.3, with 140 lectures, based on 93 reviews, and has 7491 subscribers.

You will learn about Competitive Programming Algorithms Data Structures Programming Merge Sort, QuickSort, Count Sort, Bucket Sort Linear Search, Binary Search, QuickSelect, Two Pointer Technique Stacks, Queues, Hash Tables, Hash Sets, Heap-Based Structures, Binary Search Tree-Based Data Structures, Coordinate Compression, Custom Comparators Hash Functions, Collisions, Rabin-Karp, Sliding Window Technique Greedy Algorithms Iterative Complete Search, Backtracking, Permutations, Subsets, Pruning Divide and Conquer, Binary Search the Answer, the Bisection Method Dynamic Programming: Competitive Approach, Top-Down and Bottom-Up DP, Space Optimisation, Prefix Sums, Backtracking to Find Solutions Graph Representation: Adjacency Lists, Adjacency Matrices, Edge Lists, Weighted and Unweighted Graphs Graph Exploration: Breadth-First Search (BFS), Depth-First Search (DFS), Connected Components, Multi-Source BFS Directed Graphs and Cycles: Topological Sort, Strongly Connected Components, BFS for Single-Source Shortest Paths Shortest Paths: Dijkstra, Floyd-Warshalls, Bellman-Ford and Negative-Weight Cycles, Arbitrages and the Logarithm Trick Trees: Special Properties, Faster Shortest Paths, Diameter, Minimum Spanning Trees Union-Find Disjoint Sets: Optimised Implementation, Kruskals Algorithm, Minimum Spanning Tree Variants Strings: Dynamic Programming on Strings, Longest Common Subsequence, Edit Distance, Longest Palindromic Substring, Tries, Suffix Tries Game Theory: States and Transitions, Minimax for Optimal Game Playing, Dynamic Programming To Recover Optimal Strategies Approaching Interactive Problems This course is ideal for individuals who are Students and engineers wishing to delve into competitive programming, diving into the details and soaring up the rankings or Developers with a desire to master algorithms to succeed in technical interviews or High school students with a passion for Mathematics interested in seeing algorithms applied to technology or Anyone interested in gaining insights and learning the details of algorithms and data structures It is particularly useful for Students and engineers wishing to delve into competitive programming, diving into the details and soaring up the rankings or Developers with a desire to master algorithms to succeed in technical interviews or High school students with a passion for Mathematics interested in seeing algorithms applied to technology or Anyone interested in gaining insights and learning the details of algorithms and data structures.

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Summary

Title: Competitive Programming

Price: $44.99

Average Rating: 4.3

Number of Lectures: 140

Number of Published Lectures: 140

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 140

Original Price: 94.99

Quality Status: approved

Status: Live

What You Will Learn

Competitive Programming

Algorithms

Data Structures

Programming

Merge Sort, QuickSort, Count Sort, Bucket Sort

Linear Search, Binary Search, QuickSelect, Two Pointer Technique

Stacks, Queues, Hash Tables, Hash Sets, Heap-Based Structures, Binary Search Tree-Based Data Structures, Coordinate Compression, Custom Comparators

Hash Functions, Collisions, Rabin-Karp, Sliding Window Technique

Greedy Algorithms

Iterative Complete Search, Backtracking, Permutations, Subsets, Pruning

Divide and Conquer, Binary Search the Answer, the Bisection Method

Dynamic Programming: Competitive Approach, Top-Down and Bottom-Up DP, Space Optimisation, Prefix Sums, Backtracking to Find Solutions

Graph Representation: Adjacency Lists, Adjacency Matrices, Edge Lists, Weighted and Unweighted Graphs

Graph Exploration: Breadth-First Search (BFS), Depth-First Search (DFS), Connected Components, Multi-Source BFS

Directed Graphs and Cycles: Topological Sort, Strongly Connected Components, BFS for Single-Source Shortest Paths

Shortest Paths: Dijkstra, Floyd-Warshalls, Bellman-Ford and Negative-Weight Cycles, Arbitrages and the Logarithm Trick

Trees: Special Properties, Faster Shortest Paths, Diameter, Minimum Spanning Trees

Union-Find Disjoint Sets: Optimised Implementation, Kruskals Algorithm, Minimum Spanning Tree Variants

Strings: Dynamic Programming on Strings, Longest Common Subsequence, Edit Distance, Longest Palindromic Substring, Tries, Suffix Tries

Game Theory: States and Transitions, Minimax for Optimal Game Playing, Dynamic Programming To Recover Optimal Strategies

Approaching Interactive Problems

Who Should Attend

Students and engineers wishing to delve into competitive programming, diving into the details and soaring up the rankings

Developers with a desire to master algorithms to succeed in technical interviews

High school students with a passion for Mathematics interested in seeing algorithms applied to technology

Anyone interested in gaining insights and learning the details of algorithms and data structures

Target Audiences

Students and engineers wishing to delve into competitive programming, diving into the details and soaring up the rankings

Developers with a desire to master algorithms to succeed in technical interviews

High school students with a passion for Mathematics interested in seeing algorithms applied to technology

Anyone interested in gaining insights and learning the details of algorithms and data structures

Ready to take your programming skills to the next level? In this course, which will help both novice and advanced programmers alike, you will dominate the algorithms and data structures necessary to do well in contests and to gain a competitive edge over other candidates in software interviews.

There are many tricks which are gained through experience and competitive programmers have a sixth sense when it comes to breaking problems down into the building blocks that make up a solution and which many are reluctant to share. Here I will let you in on the techniques and the applications that are useful for the field, focusing on real problems and how they are solved, while giving you an intuition on what is going on under the hood and why these ideas work.

From dynamic programming to graph algorithms and backtracking, you will get to practise and feel confident about many topics, learning advanced concepts such as union-find disjoint sets, tries and game theory without feeling lost, and to apply new content as soon as you learn it, with over 100 suggested problems, both from past olympiads and online judges and some created by me specifically for this course. All of them come with detailed solutions. With this course, you will be ready to participate in online contests and informatics olympiads, and will have the experience necessary to continue advancing in this field. Are you ready to take this big step in your journey?

Course Curriculum

Chapter 1: Introduction

Lecture 1: About the Instructor

Lecture 2: Course Prerequisites

Lecture 3: Course Structure

Lecture 4: What is an Online Judge?

Lecture 5: Online Judge Verdicts

Lecture 6: Types of Problem in Programming Contests

Lecture 7: I/O for Competitive Programming

Lecture 8: Welcome Contest

Lecture 9: Hints and Solutions

Chapter 2: Sorting

Lecture 1: Introduction to Section 2 and the Horse Race Problem

Lecture 2: Sorting in C++ Using Custom Comparators

Lecture 3: Structs and Overloading Comparison Operators

Lecture 4: Merge Sort

Lecture 5: QuickSort

Lecture 6: DNA Sorting Problem

Lecture 7: Count Sort

Lecture 8: Football Problem

Lecture 9: Bucket Sort

Lecture 10: Bucket Sort Solution to the Football Problem

Lecture 11: Problem Set 1

Lecture 12: Hints and Solutions

Chapter 3: Searching

Lecture 1: Linear Search

Lecture 2: Binary Search

Lecture 3: QuickSelect

Lecture 4: Two Pointer Technique: Start+End

Lecture 5: Two Pointer Technique: Different Paces

Lecture 6: Problem Set 2

Lecture 7: Hints and Solutions

Chapter 4: STL Data Structures: Stacks, Queues and BST- and Heap-based Structures

Lecture 1: Stacks: bracket matching

Lecture 2: Queues

Lecture 3: Hash Tables and Hash Sets Introduction

Lecture 4: Unordered (Hash) Sets and Maps in C++

Lecture 5: Ordered Sets and Maps

Lecture 6: Dictionaries

Lecture 7: Coordinate compression

Lecture 8: Custom Comparators for STL Data Structures

Lecture 9: Problem Set 3

Lecture 10: Hints and Solutions

Chapter 5: Hashing

Lecture 1: Hash Functions

Lecture 2: Extra: Handling Collisions

Lecture 3: Rabin-Karp Case Study: Pattern Search

Lecture 4: Rabin-Karp Case Study: Polynomial Rolling Hash Functions

Lecture 5: Rabin-Karp Case Study: Sliding Window Technique

Lecture 6: Sliding Window Technique: Subarray Sums

Lecture 7: Problem Set 4

Lecture 8: Hints and Solutions

Chapter 6: Greedy Algorithms

Lecture 1: What are Greedy Algorithms?

Lecture 2: Greedy Coin Change

Lecture 3: Demonstrating Correctness Under Contest Time Pressure

Lecture 4: Timetable Problem

Lecture 5: Interval Covering

Lecture 6: Sort -> Solve

Lecture 7: Problem Set 5

Lecture 8: Hints and Solutions

Chapter 7: Complete Search

Lecture 1: Complete Search Introduction

Lecture 2: Backtracking Introduction

Lecture 3: Backtracking: Permutations

Lecture 4: Backtracking: Subsets

Lecture 5: Pruning: Card Game

Lecture 6: Iterative Complete Search

Lecture 7: Problem Set 6

Lecture 8: Hints and Solutions

Chapter 8: Divide and Conquer

Lecture 1: Revisiting Sections 2 and 3

Lecture 2: Binary Search the Answer

Lecture 3: The Bisection Method

Lecture 4: Problem Set 7

Lecture 5: Hints and Solutions

Chapter 9: Dynamic Programming

Lecture 1: Top-Down Fibonacci

Lecture 2: Bottom-Up Fibonacci

Lecture 3: How to Recognise and Approach DP Problems

Lecture 4: Maximum One-Dimensional Range Sum

Lecture 5: Space Optimisation

Lecture 6: Optimal Coin Change 1

Lecture 7: Optimal Coin Change 2

Lecture 8: Backtracking to Find Solutions

Lecture 9: Prefix Sums

Lecture 10: Horses Problem Revisited

Lecture 11: Horses Addendum

Lecture 12: Problem Set 8

Lecture 13: Hints and Solutions

Chapter 10: Graphs 1: Graph Exploration

Lecture 1: Graph Terminology

Lecture 2: Graph Representation

Lecture 3: Depth-First Search

Lecture 4: Breadth-First Search

Lecture 5: Farthest Node: BFS Application

Lecture 6: Graph Sums: DFS Application

Lecture 7: Connected Components

Lecture 8: Labyrinth Problem Part 1: Single-Source BFS

Lecture 9: Labyrinth Problem Part 2: Multi-Source BFS

Lecture 10: Problem Set 9

Instructors

Blanca Huergo
Chairwoman of the Spanish Informatics Olympiad for Girls

Rating Distribution

1 stars: 5 votes

2 stars: 3 votes

3 stars: 8 votes

4 stars: 18 votes

5 stars: 59 votes

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